Demodulation of frequency-hopping spread spectrum (fhss) signals using sequential artificial intelligence/machine learning (ai/ml) models

ABSTRACT

A method includes using a first trained artificial intelligence/machine learning (AI/ML) model to identify occupied frequency channels associated with one or more incoming signals during multiple time periods, where the one or more incoming signals include a frequency-hopping spread spectrum (FHSS) signal. The method also includes generating one or more baseband signals using portions of the one or more incoming signals associated with the occupied frequency channels. The method further includes using a second trained AI/ML model to recover data from the one or more baseband signals, where the recovered data represents at least a portion of data that is encoded in the FHSS signal.

TECHNICAL FIELD

This disclosure relates generally to signal analysis systems. Morespecifically, this disclosure relates to the demodulation offrequency-hopping spread spectrum (FHSS) signals using sequentialartificial intelligence/machine learning (AI/ML) models.

BACKGROUND

Signal intelligence (SIGINT) generally refers to intelligence-gatheringoperations that occur through the interception and processing ofelectromagnetic signals or other signals. Ideally, intercepted signalscan be demodulated, and data can be recovered from the demodulatedsignals. The recovered data may then be used for any suitable purposes.Similar types of operations may occur in a number of other signalanalysis applications.

SUMMARY

This disclosure relates to the demodulation of frequency-hopping spreadspectrum (FHSS) signals using sequential artificial intelligence/machinelearning (AI/ML) models.

In a first embodiment, a method includes using a first trained AI/MLmodel to identify occupied frequency channels associated with one ormore incoming signals during multiple time periods, where the one ormore incoming signals include an FHSS signal. The method also includesgenerating one or more baseband signals using portions of the one ormore incoming signals associated with the occupied frequency channels.The method further includes using a second trained AI/ML model torecover data from the one or more baseband signals, where the recovereddata represents at least a portion of data that is encoded in the FHSSsignal.

In a second embodiment, an apparatus includes at least one processingdevice configured to use a first trained AI/ML model to identifyoccupied frequency channels associated with one or more incoming signalsduring multiple time periods, where the one or more incoming signalsinclude an FHSS signal. The at least one processing device is alsoconfigured to generate one or more baseband signals using portions ofthe one or more incoming signals associated with the occupied frequencychannels. The at least one processing device is further configured touse a second trained AI/ML model to recover data from the one or morebaseband signals, where the recovered data represents at least a portionof data that is encoded in the FHSS signal.

In a third embodiment, a non-transitory computer readable mediumcontains instructions that when executed cause at least one processor touse a first trained AI/ML model to identify occupied frequency channelsassociated with one or more incoming signals during multiple timeperiods, where the one or more incoming signals include an FHSS signal.The medium also contains instructions that when executed cause the atleast one processor to generate one or more baseband signals usingportions of the one or more incoming signals associated with theoccupied frequency channels. The medium further contains instructionsthat when executed cause the at least one processor to use a secondtrained AI/ML model to recover data from the one or more basebandsignals, where the recovered data represents at least a portion of datathat is encoded in the FHSS signal.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following description, taken in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example architecture supporting demodulation offrequency-hopping spread spectrum (FHSS) signals using sequentialartificial intelligence/machine learning (AI/ML) models according tothis disclosure;

FIG. 2 illustrates an example device supporting demodulation of FHSSsignals using sequential AI/ML models according to this disclosure;

FIGS. 3A and 3B illustrate an example baseband signal that may begenerated or otherwise obtained for training an AI/ML model used forchannel monitoring according to this disclosure;

FIG. 4 illustrates an example FHSS signal that may be generated orotherwise obtained for training an AI/ML model used for channelmonitoring according to this disclosure;

FIGS. 5 and 6 illustrate example spectrograms of FHSS signals that maybe generated or otherwise obtained for training an AI/ML model used forchannel monitoring according to this disclosure; and

FIGS. 7A and 7B illustrate example results obtained from training anAI/ML model used for channel monitoring according to this disclosure.

DETAILED DESCRIPTION

FIGS. 1 through 7B, described below, and the various embodiments used todescribe the principles of the present disclosure are by way ofillustration only and should not be construed in any way to limit thescope of this disclosure. Those skilled in the art will understand thatthe principles of the present disclosure may be implemented in any typeof suitably arranged device or system.

As noted above, signal intelligence (SIGINT) generally refers tointelligence-gathering operations that occur through the interceptionand processing of electromagnetic signals or other signals. Ideally,intercepted signals can be demodulated, and data can be recovered fromthe demodulated signals. The recovered data may then be used for anysuitable purposes. Similar types of operations may occur in a number ofother signal analysis applications.

Frequency-hopping spread spectrum (FHSS) communications refer tocommunications where radio frequency (RF) signals are transmitted whilechanging the carrier frequency used to generate the RF signals overtime. In some cases, the carrier frequency can be changed among manydistinct frequencies over a wide spectral band. When the pattern or codeused by a transmitter to change its carrier frequency is also known by areceiver, the receiver is able to receive and recover the data sent bythe transmitter using the different carrier frequencies. Among otherthings, FHSS was designed to help reduce eavesdropping by an adversaryor other party, which can be achieved since FHSS signals are typicallydifficult to characterize due to their shifting carrier frequencies.Moreover, conventional digital signal processor (DSP)-based techniquesmay struggle to demodulate FHSS signals at a level suitable forconsistent bit recovery.

This disclosure provides approaches for demodulating FHSS signals usingsequential artificial intelligence/machine learning (AI/ML) models. Asdescribed in more detail below, a series of multiple trained AI/MLmodels can be used to sequentially perform different functions in orderto demodulate and recover information from FHSS signals. For example, afirst AI/ML model (also referred to as a “channel monitor” model) canprocess information associated with one or more incoming signals andidentify whether each of multiple frequency channels within a spectrumis occupied during different time slices or other time periods. In otherwords, the first AI/ML model can be used to determine whether atransmitter is transmitting over different frequency channels duringdifferent defined time periods. As a particular example, the first AI/MLmodel may generate probabilities that frequency channels are occupied orempty during given time slices. In some cases, the first AI/ML model maybe implemented using a shallow two-layer artificial neural network(ANN), where each layer has a size of 2(m+1) and where m represents thenumber of input PSD bins (such as 1,024). Also, in some cases, the firstAI/ML model may receive power spectral density (PSD) vectorscorresponding to the time slices as inputs. In addition, in some cases,the first AI/ML model can be trained to have a low loss and can beevaluated for robustness with respect to problems such as channeloverlap.

After channel identification, the one or more incoming signals can bedetuned based on which frequency channels are occupied, such as byperforming sub-band detuning of only the occupied channels based on theoccupied channels' center frequencies, in order to produce one or morebaseband signals. A second AI/ML model (also referred to as a“demodulation” AI/ML model) can be used to extract symbols or otherencoded data from the one or more baseband signals in order to helprecover information from the incoming signal(s). This allows forrecovery of the information encoded in a captured FHSS signal or otherFHSS signal. In some cases, the second AI/ML model may be implementedusing a shallow ANN supporting a size of 2(M+1), where M represents thenumber of available symbols. Also, in some cases, the second AI/ML modelcan be trained to have a low loss and can be evaluated for robustnesswith respect to problems such as noise.

In this way, these AI/ML-based approaches can be used to effectivelyidentify FHSS signals even when the FHSS signals hop between a number offrequency channels over time. Moreover, these AI/ML-based approaches canbe used to effectively demodulate the FHSS signals and recover symbolsor other encoded data carried by the FHSS signals while providingconsistent bit recovery. Further, this can be achieved without requiringpre-existing knowledge of the FHSS modulation techniques being used andwithout requiring pre-existing knowledge of the frequency-hoppingpatterns being used by transmitters of the FHSS signals. In addition,the AI/ML models can be trained to provide high performance even incases involving issues like high signal overlap between frequencychannels, and the use of shallow network designs in some embodiments canhelp to avoid pitfalls that are common in other applications like imageprocessing.

Note that while these AI/ML-based approaches for FHSS demodulation areoften described as being used for signal intelligence purposes, theAI/ML-based approaches may be used in any other suitable applications.For example, the AI/ML-based approaches for FHSS demodulation may beused to support cognitive radio communications. In general, theAI/ML-based approaches described in this patent document may be used inany suitable device or system to support the demodulation of FHSSsignals.

FIG. 1 illustrates an example architecture 100 supporting demodulationof FHSS signals using sequential AI/ML models according to thisdisclosure. As shown in FIG. 1 , the architecture 100 receives andprocesses information defining or otherwise associated with one or moreinput signals 102. The one or more input signals 102 contain (amongother things) an FHSS signal transmitted by a transmitter over multiplefrequency channels to be demodulated by the architecture 100. Theinformation associated with the one or more input signals 102 may beobtained from any suitable source, such as a receiver. The informationassociated with the one or more input signals 102 may also have anysuitable form. In some cases, for instance, the information associatedwith the one or more input signals 102 may represent data defining amodulated frequency-hopped time-series signal. The time-series signalcan span multiple time slices, and each time slice may represent aperiod of time in which the data defining the time-series signal can beanalyzed.

In this example, the architecture 100 includes a filter delay correctionfunction 104, which generally operates to at least partially correct forany delays introduced by filtering during reception of the one or moreinput signals 102. For example, a receiver that obtains the one or moreinput signals 102 may use a finite impulse response (FIR) filter orother type of filter to smooth or otherwise pre-process the receivedsignal(s), which can introduce delays into the information associatedwith the one or more input signals 102. The filter delay correctionfunction 104 can therefore operate to substantially or completely removethe delay associated with the filter in the time-series data associatedwith the one or more input signals 102. The filter delay correctionfunction 104 may use any suitable technique to correct for delays causedby filtering of one or more signals. Note, however, that the use of thefilter delay correction function 104 is optional since there may be someinstances where filter delays are not significant or can be handled inother ways.

The information associated with the one or more input signals 102 isprovided to an AI/ML channel monitor function 106, which includes oruses a first trained AI/ML model. The AI/ML channel monitor function 106generally operates to process the information associated with the one ormore input signals 102 in order to identify which frequency channels ina given spectrum are likely occupied, meaning a transmitter istransmitting one or more signals on that frequency channel. For example,the AI/ML channel monitor function 106 may use the first AI/ML model toprocess information and determine a likelihood or probability that eachfrequency channel is occupied during each time slice. This allows theAI/ML channel monitor function 106 to identify which frequency channelsare likely occupied over time and how the usage of those frequencychannels appears to change over time. In some embodiments, theinformation associated with the one or more input signals 102 includesor is used to generate power spectral density (PSD) measurementsassociated with the one or more input signals 102, where PSDmeasurements are associated with each of the time slices. In thoseembodiments, the first AI/ML model of the AI/ML channel monitor function106 can process the PSD measurements in order to identify theprobabilities or other indicators whether each of the frequency channelsis occupied during the different time slices.

The AI/ML channel monitor function 106 includes or uses any suitableAI/ML model that has been trained to identify occupied frequencychannels. In some embodiments, for example, the AI/ML channel monitorfunction 106 may include or use an AI/ML model implemented using ashallow two-layer artificial neural network (ANN), which receives andprocesses vectors of PSD measurements corresponding to the time slices.Each layer of the ANN may have a size of 2(m+1), where m represents thenumber of input PSD bins (such as 1,024). In the following discussion,the length of the vectors containing the PSD measurements that areprovided as inputs to the AI/ML model of the AI/ML channel monitorfunction 106 can be denoted nFFT, and the total number of frequencychannels may be denoted nchannels. In some cases, the outputs of theAI/ML channel monitor function 106 include a probability that eachfrequency channel is occupied for each time slice, where theprobabilities for all frequency channels across a single time slice cansum to a value of one. Also, in some cases, the AI/ML channel monitorfunction 106 may include an AI/ML model having a linear regression layer(such as one having a size of nFFT×(2×nFFT+1)), a rectified linear unit(ReLU) activation layer, a dropout regularization layer (such as onehaving a probability of 0.6), another linear regression layer (such asone having a size of (2×nFFT+1)×(2×nFFT+1)), another ReLU activationlayer, yet another linear regression layer (such as one having a size of(2×nFFT+1)×nchannels), and a sigmoid activation layer. Further, in somecases, the AI/ML model of the AI/ML channel monitor function 106 can betrained using a mean squared error (MSE) loss function, such as with theAdamW optimizer (which may have a learning rate of 0.00001 or othersuitable value). Note, however, that any other suitable types of AI/MLmodels may be used here with the AI/ML channel monitor function 106.

Once the AI/ML model of the AI/ML channel monitor function 106 issuitably trained, the AI/ML channel monitor function 106 can be used toevaluate the information associated with the one or more input signals102 in order to identify occupied frequency channels in different timeslices. For example, the AI/ML channel monitor function 106 can processPSD measurements and generate a probability distribution for pre-definedfrequency channels for each time slice. In some cases, the frequencychannel with the highest probability may be determined to be occupied.In other cases, empty or unoccupied frequency channels may be identifiedbased on the standard deviation across the probabilities, where a highstandard deviation implies there is an occupied frequency channel and alow standard deviation implies that a frequency channel is empty. Inparticular embodiments, the threshold used with the standard deviationto differentiate between occupied and empty channels can be tuned forempty channel detection such that, when the standard deviation for afrequency channel is less than the threshold, the frequency channel isdetermined to be empty.

However the determination is made whether each frequency channel isoccupied or empty, the AI/ML channel monitor function 106 can generateoutputs 108 and 110. The outputs 108 can identify any occupied frequencychannels, and the outputs 110 can identify any empty frequency channels.The outputs 108 are used as described below to further process theinformation associated with the one or more input signals 102 in theoccupied frequency channel(s). The outputs 110 may be provided from thearchitecture 100 to any suitable destination(s) for storage or use. Eachoutput 108, 110 may have any suitable form. For example, if a frequencychannel appears to be occupied, the output 108 may identify a number orother identifier that identifies the frequency channel, or the output108 may identify a center frequency of the frequency channel. If achannel appears to be unoccupied, the output 110 may identify a timesignature corresponding to the empty channel.

The outputs 108 from the AI/ML channel monitor function 106 are providedto a baseband detuner 112, which processes the information associatedwith the one or more input signals 102 in order to detune an FHSS signalcontained in the one or more input signals 102. For example, since theoutputs 108 identify the frequency channels that are likely occupied,the baseband detuner 112 can use these channel predictions to performsub-band detuning in order to detune the portions of the one or moreinput signals 102 that may be associated with a frequency-hopped signal.As a particular example, sub-band detuning can be used for each portionof the one or more input signals 102 within the time bounds of anoccupied frequency channel. In some cases, the channel frequencies ofthe frequency channels are assumed to be known values, and the outputs108 may identify or be used to identify the center frequencies of theoccupied frequency channels. In other cases, the baseband detuner 112 oranother component of the architecture 100 may perform carrier estimationand symbol rate estimation in order to estimate the center frequency foreach occupied frequency channel and the symbol rate of each occupiedfrequency channel. The baseband detuner 112 may use any suitabletechnique to perform sub-band detuning, such as by performing a mixingfunction to down-convert portions of the one or more input signals 102to baseband.

A decimation function 114 may be used to decimate the baseband signal orsignals that are output from the baseband detuner 112. For example, thedecimation function 114 may reduce the number of samples contained ineach baseband signal for further processing. As a particular example,the decimation function 114 may reduce the number of samples containedin each baseband signal to one sample per symbol. However, this is forillustration only, and the decimation function 114 may reduce the numberof samples in the baseband signal(s) in any other suitable manner. Notethat the decimation function 114 is optional and can be excluded ifdecimation of the baseband signal(s) is not needed or required.

The decimated baseband signal or signals are provided to an AI/MLdemodulation function 116, which includes or uses a second trained AI/MLmodel. The AI/ML demodulation function 116 generally operates to processthe decimated baseband signal(s) and recover encoded data contained inthe one or more input signals 102. The recovered data represents atleast a portion of the data encoded in the one or more input signals102. For example, the AI/ML demodulation function 116 may use the secondAI/ML model to process the decimated baseband signal(s) and recoversymbols representing the encoded data contained in the one or more inputsignals 102. This allows the AI/ML demodulation function 116 to recoverinformation from an FHSS signal.

The AI/ML demodulation function 116 includes or uses any suitable AI/MLmodel that has been trained to identify symbols or other encoded datacontained in one or more baseband signals. In some embodiments, forexample, the AI/ML demodulation function 116 may include or use an AI/MLmodel implemented using a shallow ANN supporting a size of 2(M+1), whereM represents a number of available symbols used to represent encodeddata. In some cases, inputs to the AI/ML demodulation function 116include I/Q values, which represent the real and imaginary components ofthe portions of the one or more input signals 102 as converted tobaseband by the baseband detuner 112. Also, in some cases, outputs ofthe AI/ML demodulation function 116 represent one-hot vectors of lengthM. Further, in some cases, the AI/ML demodulation function 116 mayinclude a linear regression layer (such as one having a size of 2×5), atanh activation layer, and another linear regression layer (such as onehaving a size of 5×M). In addition, in some cases, the AI/MLdemodulation function 116 can be trained using an MSE loss function,such as with the AdamW optimizer. Note, however, that any other suitabletypes of AI/ML models may be used here with the AI/ML demodulationfunction 116.

Outputs 118 from the AI/ML demodulation function 116 represent datarecovered from the one or more input signals 102. For example, theoutputs 118 from the AI/ML demodulation function 116 may represent databits that are recovered based on the identified symbols contained in therelevant portions of the one or more input signals 102. The recovereddata may be provided to any suitable destination(s) for storage or use.

Note that the various functions 104-106, 112-116 shown in FIG. 1 anddescribed above may be implemented in any suitable manner. For example,in some embodiments, the functions 104-106, 112-116 shown in FIG. 1 maybe implemented or supported using one or more software applications orother software/firmware instructions that are executed by at least oneprocessor or other processing device. In other embodiments, at leastsome of the functions 104-106, 112-116 shown in FIG. 1 can beimplemented or supported using dedicated hardware components. Ingeneral, the functions 104-106, 112-116 shown in FIG. 1 and describedabove may be performed using any suitable hardware or any suitablecombination of hardware and software/firmware instructions.

Although FIG. 1 illustrates one example of an architecture 100supporting demodulation of FHSS signals using sequential AI/ML models,various changes may be made to FIG. 1 . For example, various componentsin FIG. 1 may be combined, further subdivided, replicated, omitted, orrearranged and additional components may be added according toparticular needs. Also, the various functions shown in FIG. 1 anddescribed above may be implemented using a single device or multipledevices that may or may not be geographically distributed. In addition,while shown as outputting both the outputs 110 and the outputs 118, theoutputs 110 need not be provided by the architecture 100 to any externaldestination(s).

FIG. 2 illustrates an example device 200 supporting demodulation of FHSSsignals using sequential AI/ML models according to this disclosure. Atleast one instance of the device 200 may, for example, be used toimplement at least a portion of the architecture 100 shown in FIG. 1 anddescribed above. Note, however, that the architecture 100 may beimplemented using any other suitable devices or systems. As a particularexample, the architecture 100 may be implemented using multipleinstances of the device 200, which may be positioned at or near the samegeneral location or geographically distributed over a wide (and possiblyvery large) area.

As shown in FIG. 2 , the device 200 may include at least one processingdevice 202, at least one storage device 204, at least one communicationsunit 206, and at least one input/output (I/O) unit 208. The processingdevice 202 may execute instructions that can be loaded into a memory210. The processing device 202 includes any suitable number(s) andtype(s) of processors or other processing devices in any suitablearrangement. Example types of processing devices 202 include one or moremicroprocessors, microcontrollers, digital signal processors (DSPs),application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), or discrete circuitry.

The memory 210 and a persistent storage 212 are examples of storagedevices 204, which represent any structure(s) capable of storing andfacilitating retrieval of information (such as data, program code,and/or other suitable information on a temporary or permanent basis).The memory 210 may represent a random access memory or any othersuitable volatile or non-volatile storage device(s). The persistentstorage 212 may contain one or more components or devices supportinglonger-term storage of data, such as a read only memory, hard drive,Flash memory, or optical disc. In some embodiments, one or more of thestorage devices 204 may be used to store information associated with theone or more input signals 102 and/or information generated by one ormore of the various functions 104-106, 112-116 of the architecture 100.

The communications unit 206 supports communications with other systemsor devices. For example, the communications unit 206 can include anetwork interface card or a wireless transceiver facilitatingcommunications over a wired or wireless network. As particular examples,the communications unit 206 may be used to obtain information associatedwith one or more captured signals or other input signals 102 to beanalyzed, or the communications unit 206 may be used to provide theoutputs 110 and/or 118. The communications unit 206 may supportcommunications through any suitable physical or wireless communicationlink(s).

The I/O unit 208 allows for input and output of data. For example, theI/O unit 208 may provide a connection for user input through a keyboard,mouse, keypad, touchscreen, or other suitable input device. The I/O unit208 may also send output to a display or other suitable output device.Note, however, that the I/O unit 208 may be omitted if the device 200does not require local I/O, such as when the device 200 represents aserver or other device that can be accessed remotely.

In some embodiments, instructions are executed by the processing device202 to implement the functionality of one or more functions shown inFIG. 1 and described above. For example, the instructions executed bythe processing device 202 may cause the device 200 to obtain informationassociated with one or more input signals 102, generate PSD measurementsassociated with the input signal(s) 102 (if not already contained in theinformation associated with one or more input signals 102), and providethe PSD measurements to the AI/ML channel monitor function 106 for usein identifying frequency channels that are likely occupied. Theinstructions executed by the processing device 202 may also cause thedevice 200 to perform sub-band detuning to detune occupied frequencychannels and to provide one or more baseband signals to the decimationfunction 114 for decimation or other sample reduction. The instructionsexecuted by the processing device 202 may further cause the device 200to provide the decimated baseband signal(s) to the AI/ML demodulationfunction 116 for use in recovering encoded data.

Although FIG. 2 illustrates one example of a device 200 supportingdemodulation of FHSS signals using sequential AI/ML models, variouschanges may be made to FIG. 2 . For example, computing and communicationdevices and systems come in a wide variety of configurations, and FIG. 2does not limit this disclosure to any particular computing orcommunication device or system.

The following now describes specific example details related to thetraining of the AI/ML models used in the architecture 100. For example,the following describes example ways in which the AI/ML models of theAI/ML channel monitor function 106 and the AI/ML demodulation function116 may be trained. Note, however, that these details are forillustration and explanation only and do not limit the architecture 100or its AI/ML models to any specific training techniques.

As noted above, the AI/ML model of the AI/ML channel monitor function106 can be trained to process information associated with one or moreinput signals 102 in order to identify which frequency channels in agiven spectrum are likely occupied. To train the AI/ML model of theAI/ML channel monitor function 106, the AI/ML model can be provided withtraining data, such as vectors containing PSD measurements associatedwith signals having known frequency channels that are occupied and thatare empty. Parameters of the AI/ML model can be adjusted until the AI/MLmodel accurately identifies occupied and empty frequency channels, atleast to within an acceptable margin of loss. The training data that isused here may represent any suitable training data, such as simulateddata or real-world data associated with signals having knownfrequency-hopping patterns (and therefore known occupied and emptychannels). In some embodiments, the AI/ML model of the AI/ML channelmonitor function 106 may be trained to recognize FHSS signals havingdifferent types of modulations. Example types of FHSS modulations thatthe AI/ML model of the AI/ML channel monitor function 106 can be trainedto recognize may include binary phase shift keying (BPSK), on-off keying(OOF), frequency shift keying (FSK), or quadrature amplitude modulation(QAM).

To generate simulated training data that may be used here, the followingprocedure can be performed. For each supported FHSS modulation technique(such as BPSK, OOF, FSK, and QAM), synthetic baseband signals can begenerated, where the synthetic baseband signals may be based on aspecified number of data points in each time slice, a specified samplingfrequency, and a specified bandwidth. Setting the bandwidth of thesesignals permits the creation of FHSS spectrums with control over thechannel widths and possible overlaps for later testing. Since thebandwidth can be fixed for the available modulations, the baud andsamples per symbol can vary accordingly, and upsampling by an integerfactor can be used to achieve the desired bandwidth and number of datapoints. For example, the symbols per sample (sps) may be determined as

${sps} = \frac{2f_{s}}{\Delta f}$

when generating synthetic baseband signals for BPSK, OOK, and QAM and as

${sps} = \frac{4f_{s}}{\Delta f}$

when generating synthetic BASEBAND signals for FSK. After upsampling,each signal can be low-pass filtered, such as by using a FIR filter topreserve its linear phase response. In some embodiments, the FIR filtermay have a cut-off frequency of

${1.02\frac{\Delta f}{2}},$

a stop-band attenuation of 30 dB, and a transition width of Cut-off/4.FIGS. 3A and 3B illustrate an example baseband signal that may begenerated or otherwise obtained for training an AI/ML model used forchannel monitoring according to this disclosure. More specifically, FIG.3A contains an example graph 300 plotting the real component of a BPSKsignal over time, where the BPSK signal represents a data sequencehaving bit values of [1 1 0 0 1 1 1 1 1 0 0 1 0 1 1 0 0 1 0 0], and FIG.3B contains an example graph 302 plotting a frequency of the BPSKsignal. This particular BPSK signal may have a bandwidth of about 25.6kHz after filtering.

After generating a baseband signal, an FHSS signal can be constructed.For example, individual frequency channels can be created that haveevenly-spaced carrier frequencies, where each frequency channel can spana frequency range

$f_{chan} = \frac{f_{s}}{nchan}$

(where nchan represents the number of frequency channels). In general,any suitable technique may be used to generate known frequency channelshere. For each time slice containing a specified number of bits (denotednbits), the baseband signal is tuned to a particular channel's carrierfrequency. A desired pattern of carrier frequencies can be used whenhopping between the channels, and each channel can have a specifiedbandwidth and specified modulation or be empty. FIG. 4 illustrates anexample FHSS signal that may be generated or otherwise obtained fortraining an AI/ML model used for channel monitoring according to thisdisclosure. More specifically, FIG. 4 contains an example graph 400plotting the BPSK signal of FIGS. 3A and 3B. The BPSK signal here istuned to different channel frequencies over time (note that the lowsignal at the start in FIG. 4 is due to the FIR filter delay).

After an FHSS signal has been generated, a falling raster spectrogramcan be created, where the spectrogram has a sampling frequency f andnFFT data points. FIGS. 5 and 6 illustrate example spectrograms 500 and600 of FHSS signals that may be generated or otherwise obtained fortraining an AI/ML model used for channel monitoring according to thisdisclosure. In the example shown in FIG. 5 , the spectrogram 500 isassociated with a BPSK FHSS signal that is hopped to carrier frequenciescorresponding to a channel sequence of [2, −4, 4, −3, 5, 3, −1, −5, 1,−2] or [7, 2, 9, 3, 10, 8, 5, 1, 6, 4], where the BPSK bandwidths areset to about 25.6 kHz in order to evenly divide a spectrum into tenchannels. Regions 502 in the spectrogram 500 illustrate frequencychannels that are occupied by the BPSK signal. Similarly, in the exampleshown in FIG. 6 , the spectrogram 600 is associated with a BPSK FHSSsignal that is hopped to carrier frequencies corresponding to a channelsequence of [−5, −4, −3, −2, −1, 1, 2, 3, 4, 5] or [1, 2, 3, 4, 5, 6, 7,8, 9, 10], where the BPSK bandwidths are set to about 25.6 kHz in orderto evenly divide a spectrum into ten channels. Regions 602 in thespectrogram 600 illustrate frequency channels that are occupied by theBPSK signal, and the frequency-hopping pattern is shown as repeatingthree times.

This type of training data can be used to train the AI/ML model of theAI/ML channel monitor function 106 since, as can be seen in FIGS. 5 and6 , the channels that are actually occupied can be identified. Thus, forexample, the PSDs of different time slices can be used, along with anidentification of the channels that are known to be occupied or emptyduring those time slices, to train the AI/ML model of the AI/ML channelmonitor function 106 to recognize occupied and empty channels. As aparticular example, numerous samples of PSD measurements can be labeledwith known occupied and empty channels, and the samples and their labelscan be used to train the AI/ML model of the AI/ML channel monitorfunction 106 over a number (and possibly a very large number) oftraining epochs. Ideally, the loss of the AI/ML model, which is based onthe number of incorrect occupied or empty channel estimations, decreasesover time and eventually reaches a suitably low level.

As a particular example of this training process, the AI/ML model of theAI/ML channel monitor function 106 can be trained as follows. Around tenthousand samples of signals with random modulations (such as BPSK, FSK,OOK, and QAM) at different signal-to-noise ratio (SNR) levels (such as0, 6, 12, 18, and 24 dB) with or without channel overlap can be obtainedand used to train the AI/ML model. Another ten thousand samples ofsignals with random modulations at different SNR levels with or withoutchannel overlap can then be used to test the training of the AI/MLmodel, such as to verify that the AI/ML model as trained can suitablyrecognize occupied and empty frequency channels. If channel overlap issupported (meaning the frequency range of different channels can overlapone another), any suitable amount of channel overlap may be randomlyused in the training samples.

Once the AI/ML model of the AI/ML channel monitor function 106 istrained, the AI/ML model can be placed into use in order to estimateoccupied and empty channels of additional signals. FIGS. 7A and 7Billustrate example results obtained from training an AI/ML model usedfor channel monitoring according to this disclosure. More specifically,FIG. 7A contains an example graph 700 illustrating channel occupancyprobabilities that may be generated by the AI/ML model of the AI/MLchannel monitor function 106, and FIG. 7B contains an example graph 702illustrating channel occupancy probabilities after using their standarddeviation to identify possibly empty channels. As can be seen here, theAI/ML model of the AI/ML channel monitor function 106 can identifydifferent samples as being associated with different occupied and emptyfrequency channels, while the probabilities for “channel 3” have anexcessively-high standard deviation so that this channel is identifiedas being empty.

To train the AI/ML model of the AI/ML demodulation function 116, theAI/ML model can be provided with training data, such as in the form ofI/Q samples representing numerous known symbols. Parameters of the AI/MLmodel can be adjusted until the AI/ML model accurately identifies thesymbols contained in the training data, at least to within an acceptablemargin of loss. The training data that is used here may represent anysuitable training data, such as simulated data or real-world dataassociated with signals encoding known symbols. As a particular example,one million I/Q samples with a suitable SNR level (such as about 10 dB)may be obtained for each modulation technique and used to train theAI/ML model of the AI/ML demodulation function 116. Note that while itmay be assumed the frequency channels' center frequencies are known tosupport accurate sub-band detuning, some uncertainty may exist if thefrequency channels' center frequencies are not known exactly or areestimated. The architecture 100 can operate with high effectiveness evenwith some channel frequency uncertainty, and various steps (such asperforming channel monitoring evaluation on smaller time slices) mayhelp to mitigate undesirable effects of this uncertainty. Note that thedifferent AI/ML models used here can be trained separately (i.e.independently), which can help to ensure that the different AI/ML modelsare all able to perform their respective functions effectively andaccurately.

Although FIGS. 3A through 7B illustrate examples of graphs related totraining an AI/ML model used for channel monitoring, various changes maybe made to FIGS. 3A through 7B. For example, these figures are merelymeant to illustrate examples of signals and other information that maybe generated, obtained, used, or produced during training or testing ofthe AI/ML model of the AI/ML channel monitor function 106. Any other oradditional information may be used to train the AI/ML model of the AI/MLchannel monitor function 106. Also, the specifics of the trainingtechniques described above (such as the numbers of training samples andthe types of training data) are for illustration only and can vary asneeded or desired.

The following describes example embodiments of this disclosure thatimplement the demodulation of FHSS signals using sequential AI/MLmodels. However, other embodiments may be used in accordance with theteachings of this disclosure.

In a first embodiment, a method includes using a first trained AI/MLmodel to identify occupied frequency channels associated with one ormore incoming signals during multiple time periods, where the one ormore incoming signals include an FHSS signal. The method also includesgenerating one or more baseband signals using portions of the one ormore incoming signals associated with the occupied frequency channels.The method further includes using a second trained AI/ML model torecover data from the one or more baseband signals, where the recovereddata represents at least a portion of data that is encoded in the FHSSsignal.

In a second embodiment, an apparatus includes at least one processingdevice configured to use a first trained AI/ML model to identifyoccupied frequency channels associated with one or more incoming signalsduring multiple time periods, where the one or more incoming signalsinclude an FHSS signal. The at least one processing device is alsoconfigured to generate one or more baseband signals using portions ofthe one or more incoming signals associated with the occupied frequencychannels. The at least one processing device is further configured touse a second trained AI/ML model to recover data from the one or morebaseband signals, where the recovered data represents at least a portionof data that is encoded in the FHSS signal.

In a third embodiment, a non-transitory computer readable mediumcontains instructions that when executed cause at least one processor touse a first trained AI/ML model to identify occupied frequency channelsassociated with one or more incoming signals during multiple timeperiods, where the one or more incoming signals include an FHSS signal.The medium also contains instructions that when executed cause the atleast one processor to generate one or more baseband signals usingportions of the one or more incoming signals associated with theoccupied frequency channels. The medium further contains instructionsthat when executed cause the at least one processor to use a secondtrained AI/ML model to recover data from the one or more basebandsignals, where the recovered data represents at least a portion of datathat is encoded in the FHSS signal.

Any single one or any suitable combination of the following features maybe used with the first, second, or third embodiment. The first trainedAI/ML model may use or may be configured to use PSD measurementsassociated with the one or more incoming signals to identify theoccupied frequency channels during the time periods. The first trainedAI/ML model may generate or may be configured to generate multipleprobabilities for each time period, where each probability identifies alikelihood that one of the frequency channels is occupied during theassociated time period. The one or more baseband signals may begenerated by performing sub-band detuning based on the identifiedoccupied frequency channels. The first and second trained AI/ML modelsmay include artificial neural networks. The first trained AI/ML modelmay be trained to recognize the occupied frequency channels usingsamples of signals modulated using different FHSS modulation techniquesand having known occupied or empty frequency channels, and the secondtrained AI/ML model may be trained to recover the data from the one ormore baseband signals using I/Q samples of signals containing knownsymbols. The one or more baseband signals may be decimated, and thesecond trained AI/ML model may recover or be configured to recover thedata from the one or more decimated baseband signals.

In some embodiments, various functions described in this patent documentare implemented or supported by a computer program that is formed fromcomputer readable program code and that is embodied in a computerreadable medium. The phrase “computer readable program code” includesany type of computer code, including source code, object code, andexecutable code. The phrase “computer readable medium” includes any typeof medium capable of being accessed by a computer, such as read onlymemory (ROM), random access memory (RAM), a hard disk drive (HDD), acompact disc (CD), a digital video disc (DVD), or any other type ofmemory. A “non-transitory” computer readable medium excludes wired,wireless, optical, or other communication links that transporttransitory electrical or other signals. A non-transitory computerreadable medium includes media where data can be permanently stored andmedia where data can be stored and later overwritten, such as arewritable optical disc or an erasable storage device.

It may be advantageous to set forth definitions of certain words andphrases used throughout this patent document. The terms “application”and “program” refer to one or more computer programs, softwarecomponents, sets of instructions, procedures, functions, objects,classes, instances, related data, or a portion thereof adapted forimplementation in a suitable computer code (including source code,object code, or executable code). The term “communicate,” as well asderivatives thereof, encompasses both direct and indirect communication.The terms “include” and “comprise,” as well as derivatives thereof, meaninclusion without limitation. The term “or” is inclusive, meaningand/or. The phrase “associated with,” as well as derivatives thereof,may mean to include, be included within, interconnect with, contain, becontained within, connect to or with, couple to or with, be communicablewith, cooperate with, interleave, juxtapose, be proximate to, be boundto or with, have, have a property of, have a relationship to or with, orthe like. The phrase “at least one of,” when used with a list of items,means that different combinations of one or more of the listed items maybe used, and only one item in the list may be needed. For example, “atleast one of: A, B, and C” includes any of the following combinations:A, B, C, A and B, A and C, B and C, and A and B and C.

The description in the present disclosure should not be read as implyingthat any particular element, step, or function is an essential orcritical element that must be included in the claim scope. The scope ofpatented subject matter is defined only by the allowed claims. Moreover,none of the claims invokes 35 U.S.C. § 112(f) with respect to any of theappended claims or claim elements unless the exact words “means for” or“step for” are explicitly used in the particular claim, followed by aparticiple phrase identifying a function. Use of terms such as (but notlimited to) “mechanism,” “module,” “device,” “unit,” “component,”“element,” “member,” “apparatus,” “machine,” “system,” “processor,” or“controller” within a claim is understood and intended to refer tostructures known to those skilled in the relevant art, as furthermodified or enhanced by the features of the claims themselves, and isnot intended to invoke 35 U.S.C. § 112(f).

While this disclosure has described certain embodiments and generallyassociated methods, alterations and permutations of these embodimentsand methods will be apparent to those skilled in the art. Accordingly,the above description of example embodiments does not define orconstrain this disclosure. Other changes, substitutions, and alterationsare also possible without departing from the spirit and scope of thisdisclosure, as defined by the following claims.

What is claimed is:
 1. A method comprising: using a first trainedartificial intelligence/machine learning (AI/ML) model to identifyoccupied frequency channels associated with one or more incoming signalsduring multiple time periods, the one or more incoming signals includinga frequency-hopping spread spectrum (FHSS) signal; generating one ormore baseband signals using portions of the one or more incoming signalsassociated with the occupied frequency channels; and using a secondtrained AI/ML model to recover data from the one or more basebandsignals, the recovered data representing at least a portion of data thatis encoded in the FHSS signal.
 2. The method of claim 1, wherein thefirst trained AI/ML model uses power spectral density (PSD) measurementsassociated with the one or more incoming signals to identify theoccupied frequency channels during the time periods.
 3. The method ofclaim 1, wherein the first trained AI/ML model generates multipleprobabilities for each time period, each probability identifying alikelihood that one of the frequency channels is occupied during theassociated time period.
 4. The method of claim 1, wherein generating theone or more baseband signals comprises performing sub-band detuningbased on the identified occupied frequency channels.
 5. The method ofclaim 1, wherein the first and second trained AI/ML models compriseartificial neural networks.
 6. The method of claim 1, wherein: the firsttrained AI/ML model is trained to recognize the occupied frequencychannels using samples of signals modulated using different FHSSmodulation techniques and having known occupied or empty frequencychannels; and the second trained AI/ML model is trained to recover thedata from the one or more baseband signals using I/Q samples of signalscontaining known symbols.
 7. The method of claim 1, further comprising:decimating the one or more baseband signals; wherein the second trainedAI/ML model recovers the data from the one or more decimated basebandsignals.
 8. An apparatus comprising: at least one processing deviceconfigured to: use a first trained artificial intelligence/machinelearning (AI/ML) model to identify occupied frequency channelsassociated with one or more incoming signals during multiple timeperiods, the one or more incoming signals including a frequency-hoppingspread spectrum (FHSS) signal; generate one or more baseband signalsusing portions of the one or more incoming signals associated with theoccupied frequency channels; and use a second trained AI/ML model torecover data from the one or more baseband signals, the recovered datarepresenting at least a portion of data that is encoded in the FHSSsignal.
 9. The apparatus of claim 8, wherein the first trained AI/MLmodel is configured to use power spectral density (PSD) measurementsassociated with the one or more incoming signals to identify theoccupied frequency channels during the time periods.
 10. The apparatusof claim 8, wherein the first trained AI/ML model is configured togenerate multiple probabilities for each time period, each probabilityidentifying a likelihood that one of the frequency channels is occupiedduring the associated time period.
 11. The apparatus of claim 8,wherein, to generate the one or more baseband signals, the at least oneprocessing device is configured to perform sub-band detuning based onthe identified occupied frequency channels.
 12. The apparatus of claim8, wherein the first and second trained AI/ML models comprise artificialneural networks.
 13. The apparatus of claim 8, wherein: the firsttrained AI/ML model is trained to recognize the occupied frequencychannels using samples of signals modulated using different FHSSmodulation techniques and having known occupied or empty frequencychannels; and the second trained AI/ML model is trained to recover thedata from the one or more baseband signals using I/Q samples of signalscontaining known symbols.
 14. The apparatus of claim 8, wherein: the atleast one processing device is further configured to decimate the one ormore baseband signals; and the second trained AI/ML model is configuredto recover the data from the one or more decimated baseband signals. 15.A non-transitory computer readable medium containing instructions thatwhen executed cause at least one processor to: use a first trainedartificial intelligence/machine learning (AI/ML) model to identifyoccupied frequency channels associated with one or more incoming signalsduring multiple time periods, the one or more incoming signals includinga frequency-hopping spread spectrum (FHSS) signal; generate one or morebaseband signals using portions of the one or more incoming signalsassociated with the occupied frequency channels; and use a secondtrained AI/ML model to recover data from the one or more basebandsignals, the recovered data representing at least a portion of data thatis encoded in the FHSS signal.
 16. The non-transitory computer readablemedium of claim 15, wherein the first trained AI/ML model is configuredto use power spectral density (PSD) measurements associated with the oneor more incoming signals to identify the occupied frequency channelsduring the time periods.
 17. The non-transitory computer readable mediumof claim 15, wherein the first trained AI/ML model is configured togenerate multiple probabilities for each time period, each probabilityidentifying a likelihood that one of the frequency channels is occupiedduring the associated time period.
 18. The non-transitory computerreadable medium of claim 15, wherein the instructions that when executedcause the at least one processor to generate the one or more basebandsignals comprise: instructions that when executed cause the at least oneprocessor to perform sub-band detuning based on the identified occupiedfrequency channels.
 19. The non-transitory computer readable medium ofclaim 15, wherein the first and second trained AI/ML models compriseartificial neural networks.
 20. The non-transitory computer readablemedium of claim 15, wherein: the first trained AI/ML model is trained torecognize the occupied frequency channels using samples of signalsmodulated using different FHSS modulation techniques and having knownoccupied or empty frequency channels; and the second trained AI/ML modelis trained to recover the data from the one or more baseband signalsusing I/Q samples of signals containing known symbols.